/GenX

GenX: a configurable power system capacity expansion model for studying low-carbon energy futures. More details at : https://genx.mit.edu

Primary LanguageJuliaGNU General Public License v2.0GPL-2.0

GenX

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Overview

GenX is a highly-configurable, open source electricity resource capacity expansion model that incorporates several state-of-the-art practices in electricity system planning to offer improved decision support for a changing electricity landscape.

The model was originally developed by Jesse D. Jenkins and Nestor A. Sepulveda at the Massachusetts Institute of Technology and is now jointly maintained by a team of contributors at the MIT Energy Initiative (led by Dharik Mallapragada) and the Princeton University ZERO Lab (led by Jenkins).

GenX is a constrained linear or mixed integer linear optimization model that determines the portfolio of electricity generation, storage, transmission, and demand-side resource investments and operational decisions to meet electricity demand in one or more future planning years at lowest cost, while subject to a variety of power system operational constraints, resource availability limits, and other imposed environmental, market design, and policy constraints.

GenX features a modular and transparent code structure developed in Julia + JuMP. The model is designed to be highly flexible and configurable for use in a variety of applications from academic research and technology evaluation to public policy and regulatory analysis and resource planning. Depending on the planning problem or question to be studied, GenX can be configured with varying levels of model resolution and scope, with regards to: (1) temporal resolution of time series data such as electricity demand and renewable energy availability; (2) power system operational detail and unit commitment constraints; and (3) geospatial resolution and transmission network representation. The model is also capable of representing a full range of conventional and novel electricity resources, including thermal generators, variable renewable resources (wind and solar), run-of-river, reservoir and pumped-storage hydroelectric generators, energy storage devices, demand-side flexibility, demand response, and several advanced technologies such as long-duration energy storage.

The 'main' branch is the current master branch of GenX. The various subdirectories are described below:

  1. src/ Contains the core GenX model code for reading inputs, model generation, solving and writing model outputs.

  2. Example_Systems/ Contains fully specified examples that users can use to test GenX and get familiar with its various features. Within this folder, we have two sets of examples:

  • RealSystemExample/, a detailed system representation based on ISO New England and including many different resources (upto 58)
  • SmallNewEngland/ , a simplified system consisting of 4 different resources per zone.

  1. docs/ Contains all the documentation pertaining to the model.

  2. GenXJulEnv Contains the .toml files related to setting up the Julia environment with all the specified package versions in julenv.jl.

Requirements

GenX.jl runs on Julia v1.3.0 and JuMP v0.21.3, and is currently setup to use one of the following solvers: A) Gurobi, and B) CPLEX. Note that using Gurobi and CPLEX requires a valid license on the host machine. Compatibility with open source solvers Clp and GLPK will be added shortly. The file juliaenv.jl in the parent directory lists all of the packages and their versions needed to run GenX. You can see all of the packages installed in your Julia environment and their version numbers by running pkg> status on the package manager command line in the Jula REPL.

Documentation

Detailed documentation for GenX can be found here. It includes details of each of GenX's methods, required and optional input files, and outputs. Interested users may also want to browse through prior publications that have used GenX to understand the various features of the tool. Full publication list is available here.

Running an Instance of GenX

Download or clone the GenX repository on your machine in a directory named 'GenX'. Create this new directory in a location where you wish to store the GenXJulEnv environment.

The Run.jl file in each of the example sub-folders within Example_Systems/ provides an example of how to use GenX.jl for capacity expansion modeling. The following are the main steps performed in the Run.jl script:

  1. Establish path to environment setup files and GenX source files.
  2. Read in model settings GenX_Settings.yml from the example directory.
  3. Configure solver settings.
  4. Load the model inputs from the example directory and perform time-domain clustering if required.
  5. Generate a GenX model instance.
  6. Solve the model.
  7. Write the output files to a specified directory.

Here are step-by-step instructions for running Run.jl:

  1. Start an instance of the Julia kernel.
  2. Make your present working directory to be where the Run.jl is located. To do this, you can use the Julia command julia> cd(“/path/to/directory/containing/file), using the actual pathname of the directory containing Run.jl. Note that all your inputs files should be in this directory in addition to Run.jl. Details about the required input files can be found in the documentation linked above or in the examples provided in the folder Example_Systems/. You can check your present working directory by running the command julia> pwd().
  3. Run the script by executing the command julia> include(“Run.jl”).
  4. After the script runs to completion, results will be written to a folder called “Results”, also located in the same directory as Run.jl.

Note that if you have not already installed the required Julia packages, you are using a version of JuMP other than v0.21.4, or you do not have a valid Gurobi license on your host machine, you will receive an error message and Run.jl will not run to completion.

Running Modeling to Generate Alternatives with GenX

GenX includes a modeling to generate alternatives (MGA) package that can be used to automatically enumerate a diverse set of near cost-optimal solutions to electricity system planning problems. To use the MGA algorithm, user will need to perform the following tasks:

  1. Add a Resource_Type column in the Generators_data.csv file denoting the type of each technology.
  2. Add a MGA column in the Generators_data.csv file denoting the availability of the technology.
  3. Set the ModelingToGenerateAlternatives flag in the GenX_Settings.yml file to 1.
  4. Set the ModelingtoGenerateAlternativeSlack flag in the GenX_Settings.yml file to the desirable level of slack.
  5. Create a Rand_mga_objective_coefficients.csv file to provide random objective function coefficients for each MGA iteration. For each iteration, number of rows in the Rand_mga_objective_coefficients.csv file represents the number of distinct technology types while number of columns represent the number of model zones.
  6. Solve the model using Run.jl file.

Results from the MGA algorithm would be saved in MGA_max and MGA_min folders in the Example_Systems/ folder.

Bug and feature requests and contact info

If you would like to report a bug in the code or request a feature, please use our Issue Tracker. If you're unsure or have questions on how to use GenX that are not addressed by the above documentation, please reach out to Sambuddha Chakrabarti (sc87@princeton.edu), Jesse Jenkins (jdj2@princeton.edu) or Dharik Mallapragada (dharik@mit.edu).

GenX Team

GenX has been developed jointly by researchers at the MIT Energy Initiative and the ZERO lab at Princeton University. Key contributors include Nestor A. Sepulveda, Jesse D. Jenkins, Dharik S. Mallapragada, Aaron M. Schwartz, Neha S. Patankar, Qingyu Xu, Jack Morris, Sambuddha Chakrabarti.